Automatic Classification of Abandoned Objects for Surveillance of Public Premises

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dc.contributor.author Otoom, AF
dc.contributor.author Gunes, H
dc.contributor.author Piccardi, M
dc.contributor.editor Li, D
dc.contributor.editor Deng, G
dc.contributor.editor Wang, Y
dc.date.accessioned 2010-07-15T07:28:35Z
dc.date.issued 2008-01
dc.identifier.citation The 2008 International Congress on Image and Signal Processing (CISP2008) - Vol 4, 2008, pp. 542 - 549
dc.identifier.isbn 978-0-7695-3119-9
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/12913
dc.description.abstract One of the core components of any visual surveillance system is object classification, where detected objects are classified into different categories of interest. Although in airports or train stations, abandoned objects are mainly luggage or trolleys, none of the existing works in the literature have attempted to classify or recognize trolleys. In this paper, we analyzed and classified images of trolley(s), bag(s), single person(s), and group(s) of people by using various shape features with a number of uncluttered and cluttered images and applied multi-frame integration to overcome partial occlusions and obtain better recognition results. We also tested the proposed techniques on data extracted from a well-recognized and recent data set, PETS 2007 benchmark data set [16]. Our experimental results show that the features extracted are invariant to data set and classification scheme chosen. For our four-class object recognition problem, we achieved an average recognition accuracy of 70%.
dc.format Scott McWhirter
dc.publisher IEEE
dc.relation.hasversion Accepted manuscript version en_US
dc.relation.isbasedon 10.1109/CISP.2008.688
dc.subject Abandoned object occlusion handling video surveillance
dc.subject Abandoned object occlusion handling video surveillance
dc.title Automatic Classification of Abandoned Objects for Surveillance of Public Premises
dc.type Conference Proceeding
dc.parent The 2008 International Congress on Image and Signal Processing (CISP2008) - Vol 4
dc.journal.number en_US
dc.publocation Piscataway, NJ, USA en_US
dc.publocation Piscataway, NJ, USA
dc.identifier.startpage 542 en_US
dc.identifier.endpage 549 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.conference International Congress on Image and Signal Processing
dc.for 080109 Pattern Recognition and Data Mining
dc.personcode 10273819 en_US
dc.personcode 034144 en_US
dc.personcode 020073 en_US
dc.percentage 100 en_US
dc.classification.name Pattern Recognition and Data Mining en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom International Congress on Image and Signal Processing en_US
dc.date.activity 20080527 en_US
dc.date.activity 2008-05-27
dc.location.activity Sanya, Hainan, China en_US
dc.location.activity Sanya, Hainan, China
dc.description.keywords Abandoned object occlusion handling video surveillance en_US
dc.description.keywords Abandoned object occlusion handling video surveillance
dc.staffid en_US
dc.staffid 020073 en_US
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computing and Communications


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